code: def sample_from_cp_domain_without_constraints(
      cp_domain,
      num_samples,
      domain_samplers=None,
      euclidean_sample_type="rand",
      integral_sample_type="rand",
      nn_sample_type="rand",
      discrete_euclidean_sample_type="rand",
  ):
      """Samples from the CP domain without the constraints."""
      if domain_samplers is None:
          domain_samplers = [None] * cp_domain.num_domains
      individual_domain_samples = []
      for idx, dom in enumerate(cp_domain.list_of_domains):
          if domain_samplers[idx] is not None:
              curr_domain_samples = domain_samplers[idx](num_samples)
          else:
              if dom.get_type() == "euclidean":
                  curr_domain_samples = (
                      random_sample_from_euclidean_domain(
                          dom.bounds, num_samples, euclidean_sample_type
                      )
                  )
              elif dom.get_type() == "discrete_euclidean":
                  curr_domain_samples = (
                      random_sample_from_discrete_euclidean_domain(
                          dom.list_of_items,
                          num_samples,
                          discrete_euclidean_sample_type,
                      )
                  )
              elif dom.get_type() == "integral":
                  curr_domain_samples = (
                      random_sample_from_integral_domain(
                          dom.bounds, num_samples, integral_sample_type
                      )
                  )
              elif dom.get_type() in [
                  "prod_discrete",
                  "prod_discrete_numeric",
              ]:
                  curr_domain_samples = (
                      random_sample_from_prod_discrete_domain(
                          dom.list_of_list_of_items, num_samples
                      )
                  )
              elif dom.get_type() == "neural_network":
                  from ..nn.nn_opt_utils import (
                      random_sample_from_nn_domain,
                  )
  
                  curr_domain_samples = random_sample_from_nn_domain(
                      dom.nn_type,
                      num_samples,
                      nn_sample_type,
                      dom.constraint_checker,
                  )
              elif dom.get_type() == "cartesian_product":
                  curr_domain_samples = sample_from_cp_domain(
                      dom,
                      num_samples,
                      euclidean_sample_type=euclidean_sample_type,
                      integral_sample_type=integral_sample_type,
                      nn_sample_type=nn_sample_type,
                      discrete_euclidean_sample_type=discrete_euclidean_sample_type,
                  )
              else:
                  raise ValueError(
                      "Unknown domain type %s. Provide sampler."
                      % (dom.get_type())
                  )
              individual_domain_samples.append(curr_domain_samples)
      return transpose_list_of_lists(individual_domain_samples)
  
imports: from __future__ import print_function
  from argparse import Namespace
  from copy import deepcopy
  import numpy
  from warnings import warn
  from .None import domains
  from ..parse.config_parser import config_parser
  from ..utils.general_utils import flatten_list_of_objects_and_iterables, get_original_order_from_reordered_list, transpose_list_of_lists
  from ..utils.oper_utils import random_sample_from_euclidean_domain, random_sample_from_discrete_euclidean_domain, random_sample_from_integral_domain, random_sample_from_prod_discrete_domain
label_desc: Use image node_id 16 for calling a global function with example usage: sample_from_cp_domain_without_constraints(cp_domain, num_samples, domain_samplers, euclidean_sample_type, integral_sample_type, nn_sample_type, discrete_euclidean_sample_type) and returns: transpose_list_of_lists